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Physics-informed operator learning for parameter estimation in lithium-ion-battery models enhanced by global experimental design and local identifiability analysis
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DOI：10.1016/j.egyai.2026.100847.png)
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Abstract
En 中文
• A physics-informed DeepONet is trained to approximate the Single-Particle-Model. • The PI-DeepONet generalizes over 8 scalar parameters under varying current profiles. • Good extrapolation accuracy on unseen application-specific current profiles is shown. • A novel approach for accurate estimation of model parameters is introduced.
Keywords:
Physics-informed operator learning
Deep operator network
Li-ion battery modeling
Parameter estimation
Global experimental design
Identifiability analysis
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